scholarly journals Economic Security Threat Modelling of a Commercial Bank in a Globalized Economy

2020 ◽  
pp. 142-151
Author(s):  
О.V. Dymchenko ◽  
О.О. Rudachenko ◽  
P. Gazzola
Keyword(s):  
Commercial Bank ◽  
Fuzzy Inference ◽  
A Priori ◽  
Economic Security ◽  
Security Threat ◽  
Research Information ◽  
Inference System ◽  
Security Research ◽  
Threat Modelling

In the paper, one develops a set of models for diagnosing threats to the economic security of a commercial bank, which allows improving the quality of decisions forming and making on managing the safe functioning and development of the bank. The bank's economic security research system has been developed, it includes 3 main blocks: research information space creation; assessment and analysis of the security of a commercial bank; generalization, and formation of decisions on the economic security of a commercial bank. The research made it possible to draw an inference of a theoretical, methodological, and applied nature that reflects the solution of the tasks set following the purpose of the study. A set of models has been built with modern tools of economic and mathematical modelling to improve the quality of decisions made to manage the bank's security and reduce the risks of threats. A model for calculating the bank's economic security indicator has been developed, which includes the following main stages: the construction of a structural scheme taking into account the rules of the theory of banking functioning security, then the terms and their membership functions are set for each input and output variable of the fuzzy inference system under consideration. Results of the response surface for the model are shown in the figure on the graphs of the dependence of the bank's economic security indicator on various input components. The paper requires that it is convenient to diagnose the state of economic security of a bank using fuzzy logic, this allows getting a clear quantitative representation of economic security state of the bank, as the indicators used for diagnostics may be indistinct and approximate and this a priori cannot give an adequate result when accurately calculated.

scholarly journals Aplikasi Pengambilan Keputusan dengan Metode Tsukamoto pada Penentuan Tingkat Kepuasan pelanggan

CAUCHY ◽  
2015 ◽  
Vol 4 (1) ◽  
pp. 10 ◽  
Author(s):  
Venny Riana Riana Agustin ◽  
Wahyu Henky Irawan
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Fuzzy Inference ◽  
Weighted Average ◽  
Fuzzy Rules ◽  
Quality Of Services ◽  
Inference System ◽  
A Value ◽  
Fuzzy Logic Analysis

Tsukamoto method is one method of fuzzy inference system on fuzzy logic for decision making. Steps of the decision making in this method, namely fuzzyfication (process changing the input into kabur), the establishment of fuzzy rules, fuzzy logic analysis, defuzzyfication (affirmation), as well as the conclusion and interpretation of the results. The results from this research are steps of the decision making in Tsukamoto method, namely fuzzyfication (process changing the input into kabur), the establishment of fuzzy rules by the general form IF a is A THEN B is B, fuzzy logic analysis to get alpha in every rule, defuzzyfication (affirmation) by weighted average method, as well as the conclusion and interpretation of the results. On customers at the case, in value of 16 the quality of services, the value of 17 the quality of goods, and value of 16 a price, a value of the results is 45,29063 and the level is low satisfaction

Embracing Macro-, Meso-, and Micro-Levels of Analysis of FIS-Based LMS Users' Quality of Interaction

2015 ◽  
pp. 355-387
Keyword(s):  
Online Education ◽  
Fuzzy Inference ◽  
Systemic Approach ◽  
Levels Of Analysis ◽  
Independent Group ◽  
Inference System ◽  
Time Period ◽  
Objective Interpretation ◽  
Learning Functions

An essential factor in determining the efficiency of the online education is the users' quality of interaction (QoI) with LMSs. In this chapter, the macro-meso-micro structure analysis is adopted, to examine the Fuzzy Inference System (FIS)-based approach of QoI, taking into account the LMS users' (professors' and students') interactions within a b-learning environment, in order to quantitatively estimate a normalized index of their QoI, accordingly. Additionally, for capturing the dynamics of the users interacting with the LMS, the data corresponding to a 51-week LMS Moodle usage time-period of two consequent academic years (2009/2010 and 2010/2011) at a HEI were analyzed. Finally, based on a systemic approach of the derived QoI, user-dependent/independent (group-like) (dis)similarities in LMS interaction trends, correlations, distributions and dependencies with the time-period of the LMS use are analyzed, towards an effort to contribute to a more objective interpretation of the way LMS Moodle-based b-learning functions within the HEIs.

scholarly journals A novel vertical handover algorithm based on Adap-tive Neuro-Fuzzy Inference System (ANFIS)

2018 ◽  
Vol 7 (1) ◽  
pp. 74 ◽  
Author(s):  
Eman Zakaria ◽  
Amr A.Awamry ◽  
Abdelkerim Taman ◽  
Abdelhalim Zekry
Keyword(s):  
Fuzzy Inference ◽  
Heterogeneous Wireless Networks ◽  
Access Network ◽  
Signal Strength ◽  
Vertical Handover ◽  
Inference System ◽  
Internet Connection ◽  
Neuro Fuzzy ◽  
Always Best Connected

Nowadays, there is an increased demand on an Internet connection anywhere at any time. Therefore, one has to exploit all available heterogeneous wireless networks where the target is achieving the Always Best Connected (ABC) among the different networks like UMTS, WiMAX, and WLAN. So, vertical handover techniques are used to ensure the best connectivity anywhere at any time. In this paper, novel ANFIS-based vertical handover is presented and compared with TOPSIS algorithm and other algorithms as a representative of Multi-criteria decision making (MCDM) algorithm's family. The simulation results show that the proposed handover technique provided better performance in terms of minimizing the time delay and improving the quality of service (QOS). This is because ANFIS requires iterations only in training phase otherwise, it has a much faster response. Our simulations considered the effect of many practical parameters on handover, such as subscriber speed, jitter, initial delay, bandwidth and received signal strength (RSS).According to these parameters, output values produced, which is utilized to choose the best candidate access network.

Analyzing of the sensitivity of chip formation during machining process

Sensor Review ◽  
2017 ◽  
Vol 37 (4) ◽  
pp. 448-450 ◽  
Author(s):  
Srdjan Jovic ◽  
Dragan Lazarevic ◽  
Aleksa Vulovic
Keyword(s):  
Chip Formation ◽  
Design Methodology ◽  
Fuzzy Inference ◽  
Machining Process ◽  
Machining Parameters ◽  
Important Indicator ◽  
Inference System ◽  
Content Type ◽  
Neuro Fuzzy

Purpose The paper aims to analyze chip formation during machining process since it can be a very important indicator for the quality of the machining process, as some chip forms can be undesirable. Design/methodology/approach It is essential to determine the sensitivity of the chip formation on the basis of different machining parameters. The main goal of the study was to analyze the sensitivity of the chip formation during the machining process by using adaptive neuro-fuzzy inference system (ANFIS). Findings According to the results, the chip formation is the most sensitive to feed rate. Originality/value Different cutting tests were performed to monitor the chip formation on the basis of the cutting forces and the cutting displacement. ANFIS was used to estimate the sensitivity of the chip formation during the cutting process on the basis of different parameters.

Enhancing Quality of Learning Experience Through Intelligent Agent in E-Learning

2017 ◽  
Vol 25 (01) ◽  
pp. 31-52
Author(s):  
S. Bhattacharya ◽  
S. Chowdhury ◽  
S. Roy
Keyword(s):  
Fuzzy Inference ◽  
Learning Experience ◽  
Fuzzy Rule ◽  
School Level ◽  
Rule Base ◽  
Learning Tools ◽  
Inference System ◽  
Fuzzy Rule Base ◽  
Quality Of Learning

In this paper an interactive recommending agent is proposed which helps an e-learner to enhance the quality of learning experience resulting in efficient achievement of learning objectives. The agent achieves this with the help of a fuzzy rule base working on a variety of learning materials and recommending the appropriate learning path through them. In a learner-centric environment the learning behaviour of a learner may vary to a great extent due to the characteristics of the learner and his environment. Students are often misled while choosing the appropriate path of web learning tools owing to non-availability of a human teacher/guide. By the response of a learner to different positive and negative motivation factors the proposed system employs a fuzzy machine that is fed with realization parameters e.g. Satisfied, Depressed etc. The fuzzy machine working on the paradigm of fuzzy inference system processes these realization parameters with the help of a fuzzy rule base to produce the crisp measures of the learner’s cognitive states in terms of Belief, Behaviour and Attitude. On the basis of these defuzzified crisp diagnostic parameters the proposed system will enhanced the quality of learning experience of an e-learner. To ensure this the system will provide more detailed discussion on the subject matter along with some additional learning tools. Learners often get confused to select the proper tools among various. Therefore the proposed system will also suggest most popular path among those learners with the same understanding. This recommendation comes from the analysis of data mining result. The system was tested with a wide variety of school-level students. The response obtained indicates that it is able to enhance the quality of learning experience through its recommendation.

scholarly journals Measuring Software Quality Product Based on Fuzzy Inference System Techniques in ISO Standard

2021 ◽  
Author(s):  
Atrin Barzegar
Keyword(s):  
Software Quality ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Quality Model ◽  
Inference System ◽  
Iso Standard ◽  
Software Products ◽  
Software Product ◽  
The Impact

The success of a software product depends on several factors. Given that different organizations and institutions use software products, the need to have a quality and desirable software according to the goals and needs of the organization makes measuring the quality of software products an important issue for most organizations and institutions. To be sure of having the right software. It is necessary to use a standard quality model to examine the features and sub-features for a detailed and principled study in the quality discussion. In this study, the quality of Word software was measured. Considering the importance of software quality and to have a good and usable software in terms of quality and measuring the quality of software during the study, experts and skilled in this field were used and the impact of each factor and quality characteristics. It was applied at different levels according to their opinion to make the result of measuring the quality of Word software more accurate and closer to reality. In this research, the quality of the software product is measured based on the fuzzy inference system in ISO standard. According to the results obtained in this study, it is understood that quality is a continuous and hierarchical concept and the quality of each part of the software at any stage of production can lead to high quality products.

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